Enhancement of parallel scheduling using the prioritized queuing technique for cloud sensor network

被引:0
作者
Ramesh, D. [1 ]
机构
[1] Department of CSE, University College of Engineering, Dindigul, 624622, Tamilnadu
来源
Sensor Letters | 2015年 / 13卷 / 06期
关键词
Backfilling; Parallel scheduling; PPTS; Priority queue; Task scheduling;
D O I
10.1166/sl.2015.3504
中图分类号
学科分类号
摘要
Cloud computing shares a large scale resource, storage, application and all kinds of information over a network. The cloud has to effectively schedule the requests from the customers with the available resources. Though parallel scheduling has shown a difference in maximizing the utilization cloud resources, the resource demands keep on increasing. The cloud is in the need of a better parallel scheduling algorithm to overcome the deficiencies of the existing algorithms. This paper proposes Prioritized Parallel Task Scheduling (PPTS) algorithm, which is designed to manage high priority queue and low priority queue. PPTS algorithm reduces 5%-10% of turn around time and increases node utilization up to 20%, when compared to the existing ones. This paper compares the resource utilization efficiency between AMCBF algorithm and PPTS algorithm. Copyright © 2015 American Scientific Publishers.
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页码:528 / 533
页数:5
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共 27 条
  • [1] Voorsluys W., Broberg J., Rajkumar B., Introduction to cloud computing, Cloud Computing: Principles and Paradigms, pp. 1-44, (2011)
  • [2] Vecchiola C., Pandey S., Buyya R., High-performance cloud computing: A view of scientific applications, Proceedings of the 10th International Symposium on Pervasive Systems, Algorithms and Networks I-SPAN 2009, (2009)
  • [3] Wei-Tek T., Sun X., Balasooriya J., Service-oriented cloud computing architecture, Information Technology: New Generations (ITNG), 2010 Seventh International Conference, (2010)
  • [4] Singh R., Petriya P.K., Workflow Scheduling in Cloud Computing International Journal of Computer Applications, 61, (2013)
  • [5] Gottlieb A., Almasi, George S., Highly Parallel Computing, (1989)
  • [6] Asanovic K., Bodik R., Catanzaro B.C., Gebis J.J., Husbands P., Keutzer K., Patterson D.A., Plishker W.L., Shalf J., Williams S.W., Yelick K.A., The landscape of parallel computing research: A view from Berkeley (PDF), Technical Report No. UCB/EECS-2006-183, (2006)
  • [7] Barney B., Introduction to Parallel Computing, Lawrence Livermore National Laboratory, (2007)
  • [8] Abawajy J.H., Sivarama P., Dandamudi, time/space sharing distributed job scheduling policy in a workstation cluster environment, PARELEC, IEEE Computer Society, pp. 116-120, (2000)
  • [9] Figueira S.M., Parallel Computing, 32, (2006)
  • [10] Filippopoulos D., Karatza H.D., Mathematical and Computer Modelling, 45, pp. 491-530, (2007)